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Abstract:
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Research into vaccine hesitancy is a critical component of the public health enterprise, as rates of communicable diseases that are preventable by routine childhood immunization have been increasing in recent years. It is therefore important to estimate proportions of “never-vaccinators” in various subgroups of the population in order to successfully target interventions to improve childhood vaccination rates. However, due to privacy issues, it is sometimes difficult to obtain individual patient data (IPD) to perform the appropriate time-to-event analyses: state-level immunization information services may only be willing to share aggregated data with researchers. While some existing regression methods do not require IPD, they are unable to account for either differential follow-up or a cured fraction. We propose statistical methodology for the analysis of aggregated survival data that can accommodate a cured fraction based on an approximation of the mixture cure model log-likelihood function relying only on summary statistics. We expect these methods to be applicable when there is interest in fitting cure models but where data privacy issues prevent sharing of IPD with researchers.
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